{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  CNN做MNIST分类\n",
    "refer: http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/mnist_pros.html<br/>\n",
    "@author: huangyongye\n",
    "<br/>@date: 2017-02-24\n",
    "<br/><br/>在测试的过程中出现了ResourceExhaustedError ，就是资源耗尽了！！！\n",
    "<br/>之前在keras中用同样的网络和同样的数据集来做这个例子的时候。keras占用了 5647M 的显存（训练过程中设了 validation_split = 0.2， 也就是1.2万张图）。\n",
    "\n",
    "但是我用 tensorflow 自己写的 batch=50 来测试发现呀只有529的占用显存！！！只是在最后做测试的时候因为是对10000多张图片一次性做预测才占用了 8721M 的显存<font color=\"red\">这里的测试集是 1 万张</font>。如果把预测时候的 batch 也设得比较小的话，那么整个网络只需要很小的显存了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "# 设置按需使用GPU\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "sess = tf.InteractiveSession(config=config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.导入数据，用 tensorflow 导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# 用tensorflow 导入数据\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 10)\n",
      "(55000, 10)\n"
     ]
    }
   ],
   "source": [
    "# 看看咱们样本的数量\n",
    "print mnist.test.labels.shape\n",
    "print mnist.train.labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 或者从keras中导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape= (60000, 28, 28)\n",
      "y_train.shape= (60000,)\n",
      "(60000, 28, 28)\n",
      "(60000, 10)\n"
     ]
    }
   ],
   "source": [
    "# 注意： keras 中导入数据形式不一样哦，需要根据具体情况调整\n",
    "from keras.datasets import mnist\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "print 'X_train.shape=', X_train.shape\n",
    "print 'y_train.shape=', y_train.shape\n",
    "\n",
    "# TensorFlow 类别需要使用 one-hot 类型\n",
    "from keras.utils import np_utils\n",
    "y_train = np_utils.to_categorical(y_train)\n",
    "y_test = np_utils.to_categorical(y_test)\n",
    "print X_train.shape\n",
    "print y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 构建网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 权值初始化\n",
    "def weight_variable(shape):\n",
    "    # 用正态分布来初始化权值\n",
    "    initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "def bias_variable(shape):\n",
    "    # 本例中用relu激活函数，所以用一个很小的正偏置较好\n",
    "    initial = tf.constant(0.1, shape=shape)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "# 定义卷积层\n",
    "def conv2d(x, W):\n",
    "    # 默认 strides[0]=strides[3]=1, strides[1]为x方向步长，strides[2]为y方向步长\n",
    "    return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')\n",
    "\n",
    "# pooling 层\n",
    "def max_pool_2x2(x):\n",
    "    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')\n",
    "\n",
    "\n",
    "X_ = tf.placeholder(tf.float32, [None, 784])\n",
    "y_ = tf.placeholder(tf.float32, [None, 10])\n",
    "\n",
    "# 把X转为卷积所需要的形式\n",
    "X = tf.reshape(X_, [-1, 28, 28, 1])\n",
    "# 第一层卷积：5×5×1卷积核32个 [5，5，1，32]\n",
    "W_conv1 = weight_variable([5,5,1,32])\n",
    "b_conv1 = bias_variable([32])\n",
    "h_conv1 = tf.nn.relu(conv2d(X, W_conv1) + b_conv1)\n",
    "\n",
    "# 第一个pooling 层\n",
    "h_pool1 = max_pool_2x2(h_conv1)\n",
    "\n",
    "# 第二层卷积：5×5×32卷积核64个 [5，5，32，64]\n",
    "W_conv2 = weight_variable([5,5,32,64])\n",
    "b_conv2 = bias_variable([64])\n",
    "h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n",
    "\n",
    "# 第二个pooling 层,输出[None, 7, 7, 64] ? \n",
    "h_pool2 = max_pool_2x2(h_conv2)\n",
    "\n",
    "# flatten\n",
    "h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])\n",
    "\n",
    "# fc1\n",
    "W_fc1 = weight_variable([7*7*64, 1024])\n",
    "b_fc1 = bias_variable([1024])\n",
    "h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)\n",
    "\n",
    "# dropout: 输出的维度和h_fc1一样，只是随机部分值被值为零\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n",
    "\n",
    "# 输出层\n",
    "W_fc2 = weight_variable([1024, 10])\n",
    "b_fc2 = bias_variable([10])\n",
    "y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.训练和评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 在测试的时候不使用 mini_batch， 那么测试的时候会占用较多的GPU（4497M），这在 notebook 交互式编程中是不推荐的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-5-94e05db0c125>:5: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "step 0, training accuracy 0.12\n",
      "step 1000, training accuracy 0.92\n",
      "step 2000, training accuracy 0.98\n",
      "step 3000, training accuracy 0.96\n",
      "step 4000, training accuracy 1\n",
      "step 5000, training accuracy 1\n",
      "step 6000, training accuracy 1\n",
      "step 7000, training accuracy 1\n",
      "step 8000, training accuracy 1\n",
      "step 9000, training accuracy 1\n",
      "test accuracy 0.9921\n"
     ]
    }
   ],
   "source": [
    "cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))\n",
    "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
    "\n",
    "correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "sess.run(tf.initialize_all_variables())\n",
    "for i in range(10000):\n",
    "    batch = mnist.train.next_batch(50)\n",
    "    if i%1000 == 0:\n",
    "        train_accuracy = accuracy.eval(feed_dict={\n",
    "            X_:batch[0], y_: batch[1], keep_prob: 1.0})\n",
    "        print \"step %d, training accuracy %g\"%(i, train_accuracy)\n",
    "    train_step.run(feed_dict={X_: batch[0], y_: batch[1], keep_prob: 0.5})\n",
    "\n",
    "print \"test accuracy %g\"%accuracy.eval(feed_dict={\n",
    "    X_: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 下面改成了 test 也用 mini_batch 的形式， 显存只用了 529M,所以还是很成功的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 0, training acc 0.16\n",
      "step 500, training acc 0.9\n",
      "step 1000, training acc 0.98\n",
      "step 1500, training acc 0.96\n",
      "step 2000, training acc 1\n",
      "step 2500, training acc 0.98\n",
      "step 3000, training acc 1\n",
      "step 3500, training acc 0.96\n",
      "step 4000, training acc 1\n",
      "step 4500, training acc 1\n",
      "testing step 20, test_acc_sum 19.65\n",
      "testing step 40, test_acc_sum 39.21\n",
      "testing step 60, test_acc_sum 58.86\n",
      "testing step 80, test_acc_sum 78.71\n",
      "testing step 100, test_acc_sum 98.54\n",
      " test_accuracy 0.9854\n"
     ]
    }
   ],
   "source": [
    "# 题外话：在做这个例子的过程中遇到过：资源耗尽的错误，为什么？\n",
    "# -> 因为之前每次做 train_acc  的时候用了全部的 55000 张图，显存爆了.\n",
    "\n",
    "# 1.损失函数：cross_entropy\n",
    "cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))\n",
    "# 2.优化函数：AdamOptimizer\n",
    "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
    "\n",
    "# 3.预测准确结果统计\n",
    "#　预测值中最大值（１）即分类结果，是否等于原始标签中的（１）的位置。argmax()取最大值所在的下标\n",
    "correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.arg_max(y_, 1))  \n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "\n",
    "# 如果一次性来做测试的话，可能占用的显存会比较多，所以测试的时候也可以设置较小的batch来看准确率\n",
    "test_acc_sum = tf.Variable(0.0)\n",
    "batch_acc = tf.placeholder(tf.float32)\n",
    "new_test_acc_sum = tf.add(test_acc_sum, batch_acc)\n",
    "update = tf.assign(test_acc_sum, new_test_acc_sum)\n",
    "\n",
    "# 定义了变量必须要初始化，或者下面形式\n",
    "sess.run(tf.global_variables_initializer())\n",
    "# 或者某个变量单独初始化 如：\n",
    "# x.initializer.run()\n",
    "\n",
    "# 训练\n",
    "for i in range(5000):\n",
    "    X_batch, y_batch = mnist.train.next_batch(batch_size=50)\n",
    "    if i % 500 == 0:\n",
    "        train_accuracy = accuracy.eval(feed_dict={X_: X_batch, y_: y_batch, keep_prob: 1.0})\n",
    "        print \"step %d, training acc %g\" % (i, train_accuracy)\n",
    "    train_step.run(feed_dict={X_: X_batch, y_: y_batch, keep_prob: 0.5})  \n",
    "\n",
    "# 全部训练完了再做测试，batch_size=100\n",
    "for i in range(100): \n",
    "    X_batch, y_batch = mnist.test.next_batch(batch_size=100)\n",
    "    test_acc = accuracy.eval(feed_dict={X_: X_batch, y_: y_batch, keep_prob: 1.0})\n",
    "    update.eval(feed_dict={batch_acc: test_acc})\n",
    "    if (i+1) % 20 == 0:\n",
    "        print \"testing step %d, test_acc_sum %g\" % (i+1, test_acc_sum.eval())\n",
    "print \" test_accuracy %g\" % (test_acc_sum.eval() / 100.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 查看网络中间结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 在学习 CNN 的过程中，老是看到他们用图片的形式展示了中间层卷积的输出。好吧，这下我必须得自己实现以下看看呀！！！</b>\n",
    "<br/> 关于 python 图片操作主要有 matplotlib 和 PIL 两个库（refer to: http://www.cnblogs.com/yinxiangnan-charles/p/5928689.html）。\n",
    "<br/>我们使用 matplotlib 来完成这个任务。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 图像操作基础"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]\n",
      "img_data shape = (784,)\n"
     ]
    }
   ],
   "source": [
    "# 我们先来看看数据是什么样的\n",
    "img1 = mnist.train.images[1]\n",
    "label1 = mnist.train.labels[1]\n",
    "print label1  # 所以这个是数字 6 的图片\n",
    "print 'img_data shape =', img1.shape  # 我们需要把它转为 28 * 28 的矩阵\n",
    "img1.shape = [28, 28]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# import matplotlib.image as mpimg  # 用于读取图片，这里用不上\n",
    "\n",
    "print img1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NPwT/3C9CXHpOuNPpfAtLF9wyGW0r6L1g71sTxsrsFlyKuAhDSgwxVfGNMy5VG6Kv5zC3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\nsIjIjhTCIiI7+id9QXXiW+c4owAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f996ee05e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img1)\n",
    "plt.axis('off') # 不显示坐标轴\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.imshow?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "好吧，是显示了图片，但是结果是热度图像。我们想显示的是灰度图像。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mxLyQCIvGcGSE9wlHG3M7OzvhxpxvU5SL/Y3qQnifcFSknesEe58wl6bMFeXh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\nhOgQibAQQnSIRFgIITpEIiyEEB3y/wF8evYwyV4p+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f996e57d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我们可以通过设置 cmap 参数来显示灰度图\n",
    "plt.imshow(img1, cmap='gray') # 'hot' 是热度图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们想看 Conv1 层的32个卷积滤波后的结果，显示在同一张图上。 python 中也有 plt.subplot(121) 这样的方法来帮我们解决这个问题。如下：先看两个试试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.subplot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 784)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9963199a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(4,8,1)\n",
    "plt.imshow(img1, cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.subplot(4,8,2)\n",
    "plt.imshow(img1, cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 显示网络中间结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "好了，有了前面的图像操作基础，我们就该试试吧！！！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28, 32)\n",
      "<type 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# 首先应该把 img1 转为正确的shape (None, 784)\n",
    "X_img = img1.reshape([-1, 784])\n",
    "y_img = mnist.train.labels[1].reshape([-1, 10])\n",
    "# 我们要看 Conv1 的结果，即 h_conv1\n",
    "result = h_conv1.eval(feed_dict={X_: X_img, y_: y_img, keep_prob: 1.0})\n",
    "print result.shape\n",
    "print type(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "好的，我们成功的计算得到了 h_conv1，那么赶紧 imshow() 看看吧！！！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PttPpWIbt4EHG1jW/3w+fzydr8fl8kv2Hw2EEAoGlAKDX68nlT+0H2+X4xcNl1eWsy/oN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YyYgQLdDfm70/GCpFmPehGqFDmzCU70U+v84XBY/A+m06n4VlCUpOPzMoClL8dZNr/0CnC73ZjN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f996312efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for _ in xrange(32):\n",
    "    show_img = result[:,:,:,_]\n",
    "    show_img.shape = [28, 28]\n",
    "    plt.subplot(4, 8, _ + 1)\n",
    "    plt.imshow(show_img, cmap='gray')\n",
    "    plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "哈哈，成功啦！从上面的结果中，我们可以看到不同的滤波器（卷积核）学习到了不同的特征。比如第一行中，第一个滤波器学习到了边缘信息，第5个卷积核，则学习到了骨干的信息。感觉好有趣，不由自主的想对另一个数字看看。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  1.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  1.  0.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  1.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  1.]]\n"
     ]
    }
   ],
   "source": [
    "# 输出前10个看看，我选择数字 9 来试试\n",
    "print mnist.train.labels[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5dqaJrBY80tuhMIpiT/bKp3JY9Y7ZM4DVLhxWRVVyr9eTaBHLXJlvtVoZzwgA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ofH45GaIqMUdQ8bifl8jsFggHK5LCnhTaCjR517DsyZz+fw+XxCpHvd32GGyMhLYv2LlwYzPYyE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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f996237d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 首先应该把 img1 转为正确的shape (None, 784)\n",
    "X_img = mnist.train.images[2].reshape([-1, 784])\n",
    "y_img = mnist.train.labels[1].reshape([-1, 10]) # 这个标签只要维度一致就行了\n",
    "result = h_conv1.eval(feed_dict={X_: X_img, y_: y_img, keep_prob: 1.0})\n",
    "\n",
    "for _ in xrange(32):\n",
    "    show_img = result[:,:,:,_]\n",
    "    show_img.shape = [28, 28]\n",
    "    plt.subplot(4, 8, _ + 1)\n",
    "    plt.imshow(show_img, cmap='gray')\n",
    "    plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第一个核还是主要学习到了边缘特征，第五个核还是学到了骨干特征（当然在某种程度上）。好吧，本次就到这啦！"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
